BTC risk indicator

Disclaimer

This is a work in progress. It is also untested and not in any way financial advice.

Main idea

Combine multiple metrics in an attempt to predict local tops and bottoms.

Can be used as a buy / sell signal or as a way to dynamically DCA based on how 'risky' current price level is.

What's in this repo?

There are 2 pine scripts in here, both are indicators designed to work with TradingView.

It's the same logic in both, one of them in an overlay and the other one is an oscillator scaled to the 0..1 range.

Preview

How to use it

It's been published on TradingView as an indicator, just search for:

• Risk Metric: BTCUSD and/or
• Risk Metric: BTCUSD Overlay

Update: they got taken down, apparently references to the github project are not allowed (pretty lame), will republish next month. For now you can just copy and paste the scripts in the pine editor.

Metrics involved so far, open to suggestions for more.

• EMA 5 Daily / EMA 50 Weekly
• Mayer Multiple
• Sharpe Ratio
• Price / EMA 400
• RSI 20
• Puell Multiple
• Power Law
• Sortino Ratio
• Bitcoin google trend numbers
• RUPL/NUPL

Current logic

Average the above metrics and then try to adjust / correct the average (explained below).

Main problem

Because of the logarithmic nature of BTC growth and diminishing returns the peaks of our oscillator get lower and lower and the bottoms get higher and higher. We are also dealing with data (prices) that span multiple orders of magnitude which is not ideal when mapping to the 0..1 range.

Current logic to adjust / correct the average

Multiplying by the log of the linear regression and by the log of average true range.

multi = math.log(lr) * math.log(ta.atr(5)) // feels too arbitrary, open to suggestions

Effect of the adjustment (light blue is the 'before')

The peaks look okay after the adjustment but the bottoms don't go below 0.25 after 2013 or so, open to suggestions on how to adjust it better.

TODO list / current ideas:

• Optimize rolling window length of various oscillators
• Figure out what other metrics we can use, maybe MACD etc
• Figure out a better way to adjust for diminishing returns and the logarithmic nature of BTC growth
• Weighted average + optimization of the weights by
  • neural networks
  • simulated annealing
  • monte carlo simmulations
  • genetic algorithms
• Run backtests and calculate net profit
• Use net profit as a fitness function to 'evolve' a better indicator automagically (a combination of the above 2)

Contributing

If you have any suggestions or ideas for improvement please get in touch.

Feel free to email me. You can also just open a PR.

I am just now getting familiar with pine script and I am also not an expert in this field so there's likely plenty of room for improvement.

Credits

Inspired by Benjamin Cowen's BTC risk idea.

Special thanks to M. Almusimie for working out most of the oscillators involved and Jay for suggestions.

If you find this useful please consider supporting this project.

	Address
BTC	3B1gtQhVQj1x5c1jzVHHzq2eGKyEqf7rQb
ETH	0x18Df0b2C16d3E3927Ef74e02268fE36949bb6b2c
ADA	addr1vyqvaqs56d90ha55duh04eh59y7fcps8duplgm987nyhv9qp3n05f
USDT	0x96A37296B30Bf19A917c34810b3f040192Be2e67